ConsisID
Identity-preserving text-to-video generative model based on frequency decomposition
Product Details
ConsisID is an identity-preserving text-to-video generation model based on frequency decomposition, which generates high-fidelity videos that are consistent with the input text description by using identity control signals in the frequency domain. The model does not require tedious fine-tuning for different cases and is able to maintain the consistency of the identities of the people in the generated videos. The proposal of ConsisID has promoted the development of video generation technology, especially in terms of process without adjustment and frequency-aware identity maintenance control scheme.
Main Features
How to Use
Target Users
ConsisID is targeted at researchers and developers in the field of video generation, specifically those interested in generating high-fidelity videos that match text descriptions. The technology can be applied to video content creation, virtual reality, augmented reality, and any scenario where a video that matches a specific text description needs to be generated.
Examples
Generate videos depicting specific character traits for use in movie previews or game character creation.
Generate news broadcast videos based on press releases to improve the efficiency of news production.
Create virtual anchors for live broadcast or online education platforms.
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